Customer data analysis gives energy suppliers the opportunity to better understand their customers. It is therefore one of the central tasks of the strategic approach of Customer Centricity. This approach places the customer and his needs at the center of the company's business orientation. This is significant for energy suppliers because they are not only confronted with the energy transition and digitalization. They also face the challenges of constantly growing competition, changing business models and declining revenues from electricity, gas or heat. This is because more and more customers are advancing to become so-called prosumers, who supply themselves with energy. As a result, they are buying less electricity from their energy suppliers. At the same time, demand for energy services is growing. In addition, there is the trend of an increasing willingness to change energy customers, which is accompanied by increased demands on quality and customer service.
In order to retain customers in the long term and minimize churn, a deeper understanding of them and their needs is necessary. This is where customer data analysis provides support.
Big Data as a prerequisite for customer data analysis.
Without Big Data, there is no customer data analytics. But Big Data is a broad term. On the one hand, it describes the growing amount of data in companies. On the other hand, the term encompasses modern technologies with which such huge amounts of data can be captured and processed. Targeted analysis methods based on artificial intelligence and machine learning are used here. They can be used to identify patterns, correlations and other useful information. The targeted use of such analytical methods to evaluate customer data provides marketing and sales departments in energy companies with new access and scope to redesign their offerings, customer service and sales. In view of the fact that services in the energy industry are increasingly being provided digitally, flexibly and networked, customer data analysis can also be used to address changes in customer behavior. Customer data analysis takes into account digital communication channels, customer portals, automated mass processes, the development of customer relationship management systems for the creation of offers tailored to specific target groups, and enables the integration of new services and products into an existing portfolio. It also helps in dealing with customer churn.
Only targeted customer data analysis provides advantages.
This is because energy providers have a lot of customer data. Customer data includes the entire amount of information a company has about its customers. In B2C, for example, it includes information about customer master data, buying behavior, budget, sales and sales potential, and behavioral data such as tracking or product usage. In B2B, data includes company size, industry, buying behavior, sales and sales potential, suppliers, and other collaborative relationships. In addition, all processes and workflows in the company generate data. The challenge, however, is to make profitable and meaningful use of all this existing data in the company.
On the one hand, a strategic approach within the framework of customer data analysis helps here. On the other hand, there are digital tools for customer data analysis. However, in order to be able to use these effectively, a targeted approach is required as well as a precise understanding of the goals that are to be achieved through the customer data analysis. To this end, fundamental questions need to be clarified in advance, such as which data should be collected and evaluated for what purpose. To what extent data is available, which analysis methods and tools can be used, which skills and qualifications are required for the customer data analysis. Thus, only a targeted implementation of customer data analysis pays off for the company's goals and guarantees for an efficient use of valuable company resources.
Selected areas of application for customer data analysis.
For example, in order to better tailor offers and products to specific customer segments, improve service offerings or reduce losses in the customer base, results from customer data analyses can be used specifically for customer targeting, new customer acquisition or customer retention. Customer data analysis can be used to determine the value of a customer. Customer segmentation and ABC analysis provide insight into which customers are particularly important for the company.
Customer lifetime value (CLV) can also be used to determine the expected customer value for the company over the duration of the business relationship. The CLV belongs to the customer value analyses and is one of the most important parameters for marketing control. The CVL provides the company with sales forecasts based on the example of each individual customer for individual assortments or the entire product and service range. Furthermore, persona concepts can be developed on the basis of the customer data analysis. These are fictitious persons. They stand as a representative example for the target group and are described with corresponding characteristics and needs, which the company specifically addresses. Customer data analyses also provide valuable information for reacting in good time to customer churn and the termination of contracts. After all, losses in the customer base can have a significant impact on the development of sales and profits and thus on the company. The task of churn management is to identify the reasons for customer churn in good time and to react to them. For example, a data-driven churn prediction can predict customers at risk of churn. Companies can respond to these with targeted campaigns to revive customer relationships and make them appropriate offers.
Conclusion: what customer data analytics can do for energy providers.
This small selection of examples alone illustrates what customer data analytics enables companies in the energy industry to do. They can proactively approach customers and face growing competition with a holistic 360 degree understanding of customer wants and needs. Customer data analytics enable them to create offerings and services that put customers first and strengthen customer relationships. This results in satisfied customers and reduces the risk of customer churn. In this way, customer data analyses create transparency and new options for action for energy suppliers. At the same time, customer data analysis is a demanding undertaking, as it requires both technical and mathematical expertise as well as a fundamental understanding of marketing and sales.
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